activity
20152023
most citedNeural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoods

11 citations · 27 across the 8 of their papers we have counts for

collaborators

11 papers

stat.AP2023

Statistical constraints on climate model parameters using a scalable cloud-based inference framework

James Carzon, Bruno R. de Abreu, Leighton Regayre +5

Atmospheric aerosols influence the Earth's climate, primarily by affecting cloud formation and scattering visible radiation. However, aerosol-related physical processes in climate…

stat.ME2023★ 11 cited

Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoods

Julia Walchessen, Amanda Lenzi, Mikael Kuusela

In spatial statistics, fast and accurate parameter estimation, coupled with a reliable means of uncertainty quantification, can be challenging when fitting a spatial process to rea…

stat.AP2022

Background Modeling for Double Higgs Boson Production: Density Ratios and Optimal Transport

Tudor Manole, Patrick Bryant, John Alison +2

We study the problem of data-driven background estimation, arising in the search of physics signals predicted by the Standard Model at the Large Hadron Collider. Our work is motiva…

stat.ML2022★ 2 cited

Simulator-Based Inference with Waldo: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems

Luca Masserano, Tommaso Dorigo, Rafael Izbicki +2

Prediction algorithms, such as deep neural networks (DNNs), are used in many domain sciences to directly estimate internal parameters of interest in simulator-based models, especia…

stat.AP2021★ 8 cited

Uncertainty quantification for wide-bin unfolding: one-at-a-time strict bounds and prior-optimized confidence intervals

Michael Stanley, Pratik Patil, Mikael Kuusela

Unfolding is an ill-posed inverse problem in particle physics aiming to infer a true particle-level spectrum from smeared detector-level data. For computational and practical reaso…

stat.AP2021

Spatio-temporal Local Interpolation of Global Ocean Heat Transport using Argo Floats: A Debiased Latent Gaussian Process Approach

Beomjo Park, Mikael Kuusela, Donata Giglio +1

The world ocean plays a key role in redistributing heat in the climate system and hence in regulating Earth's climate. Yet statistical analysis of ocean heat transport suffers from…